The present invention relates generally to machine systems, such as locomotives and other systems, having a plurality of subsystems, and, more particularly, to a system and process for developing diagnostics algorithms for predicting impending failures of the subsystems in the locomotive.
As will be appreciated by those skilled in the art, a locomotive is a complex electromechanical system comprised of several complex subsystems. Each of these subsystems is built from components which over time fail. The ability to automatically predict failures before they occur in the locomotive subsystems is desirable for several reasons, such as reducing the occurrence of primary failures which result in stoppage of cargo and passenger transportation. These failures can be very expensive in terms of lost revenue due to delayed cargo delivery, lost productivity of passengers, other trains delayed due to the failed one, and expensive on-site repair of the failed locomotive. Further, some of those primary failures could result in secondary failures that in turn damage other subsystems and/or components. It will be further appreciated that the ability to predict failures before they occur in the various subsystems would allow for conducting condition-based maintenance, that is, maintenance conveniently scheduled at the most appropriate time based on statistically and probabilistically meaningful information, as opposed to maintenance performed regardless of the actual condition of the subsystems, such as would be the case if the maintenance is routinely performed independently of whether the subsystem actually needs the maintenance or not. Needless to say, a condition-based maintenance is believed to result in a more economically efficient operation and maintenance of the locomotive due to substantially large savings in cost. Further, such type of proactive and high-quality maintenance will create an immeasurable, but very real, good will generated due to increased customer satisfaction. For example, each customer is likely to experience improved transportation and maintenance operations that are even more efficiently and reliably conducted while keeping costs affordable since a condition-based maintenance of the locomotive will simultaneously result in lowering maintenance cost and improving locomotive reliability.
Previous attempts to overcome the above-mentioned issues have been generally limited to diagnostics after a problem has occurred, as opposed to prognostics, that is, predicting a failure prior to its occurrence. For example, previous attempts to diagnose problems occurring in a locomotive have been performed by experienced personnel who have in-depth individual training and experience in working with locomotives. Typically, these experienced individuals use available information that has been recorded in a log. Looking through the log, the experienced individuals use their accumulated experience and training in mapping incidents occurring in locomotive subsystems to problems that may be causing the incidents. If the incident-problem scenario is simple, then this approach works fairly well for diagnosing problems. However, if the incident-problem scenario is complex, then it is very difficult to diagnose and correct any failures associated with the incident and much less to prognosticate the problems before they occur.
Presently, some computer-based systems are being used to automatically diagnose problems in a locomotive in order to overcome some of the disadvantages associated with completely relying on experienced personnel. Once again, the emphasis on such computer-based systems is to diagnose problems upon their occurrence, as opposed to prognosticating the problems before they occur. Typically, such computer-based systems have utilized a mapping between the observed symptoms of the failures and the equipment problems using techniques such as a table look up, a symptom-problem matrix, and production rules. Unfortunately, as suggested above, the usefulness of these techniques have been generally limited to diagnostics and thus even such computer-based systems have not been able to provide any effective solution to being able to predict failures before they occur.
In view of the above-mentioned considerations, there is a general need to be able to quickly and efficiently prognosticate any failures likely to occur in any of the subsystems of the machine, while minimizing the need for human interaction and optimizing the repair and maintenance needs of the subsystem so as to be able to take corrective action before any actual failure occurs.
Generally speaking, the present invention fulfills the foregoing needs by providing a process for systematically developing algorithms for predicting failures in a system, such as a locomotive, having a plurality of subsystems. The process allows for conducting a failure mode analysis for a respective subsystem so as to identify target failure modes of the subsystem and/or collecting expert data relative to the respective subsystem. The process further allows for identifying, based on the identified failure modes and/or the collected expert data, one or more signals to be monitored for measuring performance of the respective subsystem. A generating step allows for generating, based on the monitored signals, a predicting signal indicative of the presence of any target failure modes in the respective subsystem.
The present invention further fulfills the foregoing needs by providing a system for developing algorithms for predicting failures in a machine having a plurality of subsystems. The system includes a module, e.g., a programmable module, configured to conduct a failure mode analysis for a respective subsystem so as to identify target failure modes of the subsystem, and/or configured to collect expert data relative to the subsystem. An identifier module is configured to identify, based on the identified target failure modes and/or collected expert data, one or more signals to be monitored for measuring performance of the respective subsystem, and a detection module is coupled to receive the monitored signals for generating a predicting signal indicative of the presence of any target failure modes in the respective subsystem.